source:https://www.epa.gov/heatislands/learn-about-heat-islands
This week’s assignment will encompass the following concepts covered in Class 12 lecture & lab:
Class 12 Lecture and Assignment Materials:
C12 Data Package
Note: The
C12 Data Pacakgefeatures the ARD grid utilized early in the assignment to determine the horizontal and vertical position of the assignment AOI. Once done, two empty folders are provided for the surface temperature data (ARD_ST) and the surface reflectance data (ARD_SR). When accessing data at EarthExplorer, the results can be placed in the ARD_ST and ARD_SR folders, respectively, to keep assignment organization.
-While there are many contextual references in the assignment introduction below, there are no required readings this week, and there will be NO quiz for this week’s materials.
Assignment Introduction: In the era of climate change, there has been significant research into the earth’s surface temperature, especially in urban areas where socio-economic, landcover, historical urban design all strongly correlate with increasing surface temperatures - often referred to as Urban Heat Islands.
Through the socio-economic lens, income is often correlated with vegetative cover in urban areas:
Urban Correlations Between Vegetative Cover & Income
Redlining & Vegetative Cover
Particularly relevant for this assignment, rising surface temperatures are closely correlated with landcover - all of which can be measured with a fair amount of precision at the municipal level via the Landsat program:
Satellites Pinpoint Drivers of Urban Heat Islands in the Northeast
Widespread Race and Class Disparities in Surface Urban Heat Extremes Across the United States
Landcover and Surface Temperature in Urban Environments
Various heat-producing surfaces can be found in the urban environment and categorized into landcover types. This research can be helpful in interpreting the results of remote sensing for both surface temperature and landcover:
Landcover Types in Urban Environment Exhibiting Different Surface Temperature Profiles
To Start, this assignment will rely on both the general process and sample imagery found in this week’s lecture demo. Here the city of Springfield, MO is the AOI; the imagery date is the height of summer on June 20th, 2021 at approximately 10:50 am (more on how to determine time of the imagery later in the assignment).
For your own assignment, you will pick a US city as your AOI. Good picks are cities that are mid-sized to large. They must first meet the following criteria:
The Springfield, MO AOI meets both criteria as seen below:
h and v values in the attribute table for the grid tile. In the case of the grid tile containing Springfield, MO, the h value is 18 and the v value is 11.ARD Grid overlay to QuickMapServices Google Maps Baselayer
Landsat C2 US ARD
18, and Vertical ID of 11:Horizontal/Vertical Position ID of AOI ARD Tile
This Tile is cloud-free, full coverage over the AOI, and is within the growing season and the high summer - 6/20/21
ARD_SR and ARD_ST, respectively:Surface Reflectance Bundle
Surface Temperature Bundle
outputs for processing outputs upcoming in the assignment:Assignment Data Structure
Azimuth & Elevation Diagram
Landsat data acquisition times are expressed in Greenwich Mean Time (GMT), and can be converted to CST, PST and EST. Since Springfield, MO is CST, we subtract 6 hours from the GMT expression in the metadata:
We can also find the weather of a certain day for a certain city:
For Springfield, MO on the data of imagery acquistion (6-20-21) the temperature profile is as follows (hot by 10am!):
Temperature Profile on Date of Image Acquisition
To Find the Date, Time, Elevation and Azimuth, access the .json file located in either the ARD_ST folder:
.json metadata file for ARD tile scene
Sections Highlighted in Magenta Include Date, Time, Elevation and Azimuth
From this information, we can place the assignment example for Springfield, MO as follows:
In effect, if we were standing facing south in Springfield, MO when this Landsat scene was recorded it would have been a hot morning approach 90 degrees; the sun would have been relatively high in the sky approaching a typical ‘noon’ position at 90 degrees; and the sun’s east>west orientation would be southeast in the sky. Indeed, these conditions are pretty good to represent a typical ‘early, hot summer day’ in the midwest of the US in the city of Springfield, MO. This is the condition that we are seeking for our surface temperature analysis upcoming in the assignment. It is also appropriate for the vegetative cover analysis where we would expect the tree coverage to represent a ‘full-leaf’ summer day.
NDVI = (NIR Band - Red Band / (NIR Band + Red Band)
NDVI = (Band 5 – Band 4) / (Band 5 + Band 4)
Utilize Bands 4 and 5 for NDVI
ARD_SR folder:("LC08_CU_018011_20210620_20210703_02_SR_B5@1"-"LC08_CU_018011_20210620_20210703_02_SR_B4@1")/("LC08_CU_018011_20210620_20210703_02_SR_B5@1"+"LC08_CU_018011_20210620_20210703_02_SR_B4@1")
NDVI low > high Symbolization
Save the results as your_city_name_NDVI.tif into the outputs folder. You can also save a QGIS project into the assignment folder before proceeding to the next steps.
ST layer in the ARD_ST folder. Import this band layer into QGIS:ST_B10 Layer
Loaded to QGIS Layers Panel
Surface Temperature (ST) – Represents the temperature of the Earth’s surface in Kelvin (K).
Kelvin Values for the ST band
Application of Scale and Offset
More information on Scaling HERE
ST Band 10 in QGIS, Layers Panel:
Note the unscaled Kelvin Values 28804 - 49411
Here the digital numbers need to be scaled accordingly, and an offset of 149 is also applied. If the value is to be expressed in Celcius or Fahrenheit, a further conversion needs to take place via the raster calculator. This will be accomplished in two steps:
Scale the raster values in raster calculator
Apply the following temperature conversion where K is the scaled raster values and F is the Fahrenheit temperature measurement:
F = 1.8(K - 273) + 32
To Start, open the Raster Calculator and apply the following scale factor and offset value as noted in the product metadata, exporting the product to the outputsdata folder as st_scaled:
"LC08_CU_018011_20210620_20210703_02_ST_B10@1" * 0.00341802 + 149Scaled + Offset Kelvin Values
Next, apply the temperature conversion based on the formula for kelvin to fahrenheit units, and output as your_city_name_ST in the typical, default GeoTiff format.
1.8 * ("st_scaled@1" - 273) + 32Review the resulting raster values, Kelvin vs Fahrenheit:
Kelvin vs Fahrenheit
Utlize Value Tool & QuickMapServices Base Imagery to Evaluate Resulting Surface Temperatures Across the Urban Extent
At this juncture, both the NDVI and Surface Temperature Analysis Rasters have been created; but there extent is simply the extent of the original Landsat tile edge. As the goal of the assignment is to compare and evaluate the vegetative cover within the respective urban AOI relative to surface temperature, ‘cutting’, i.e. masking to the city AOI is valuable. To do so, we will query OpenStreetMap for the ID for the respective city; acquire this ID in Overpass Turbo and utilize it as polygon mask in QGIS. We’ve done this type of query during the Lecture Demo 10.
To Start, navigate to OpenStreetMap, and search for the city by name:
Search by City Name and State
ID of Feature in OSM
/*
This query looks for a node, way or relation
with the given id.
*/
[out:json][timeout:25];
// gather results
(
// query part for: “id:141244”
relation(141244);
);
// print results
out body;
>;
out skel qt;
Overpass Turbo Query Result for Springfield, MO
Download the result as GeoJSON and place into the outputs folder.
Import the GeoJSON file into QGIS:
OSM Springfield, MO Overpass Turbo Query Feature Loaded to QGIS
export feature through the Fix Geometries tool via the Processing Toolbox and produce a temporary layer:Fix Geometries
outputs folder as NDVI_final.tif and ST_final.tif, respectively:Process the ST layer, then the NDVI layer to the Fix Geometries Mask using the parameters in the tool
Correctly Masked Layers for NDVI and ST Values
With both Raster Layers Prepared, save the QGIS project before proceeding. The map layout will feature a side-by-side comparison of the NDVI results to the Surface Temperature results. A Gradient Color Ramp will be provided for each raster theme; both will be displayed with the same map scale, so 1 scale bar can apply to both map layout frames.
To Start, like Class 5 assignment, create a Gradient Color Ramp for the NDVI Spectral Index and Surface Temperature Gradient using the following short video as guide:
Pseudocolor Color Ramps Applied to Each Analysis Layer
Note: Make sure to apply a pseudocolor color ramp for the legend development. Like assignment 5, below is a short video outlining the process in QGIS LTR 3.1. In later releases - 3.2x - the default symbolization for rasters is indeed a pseudocolor color ramp, and thus no need to develop a secondary gradient color ramp in the layout.
Page Properties for Landscape Orientation
Locking the Layer within the Map Frame
Assignment 12 Draft Layout Example
Note: in the layout example provided, both legends are oriented with low values at top, high values at bottom. There’s a strong argument to flip these legends so they correspond better. Low NDVI values correspond with high surface temperatures; it would be appropriate and logical to flip the surface temperature legend so that its high values are on top and correspond with the low NDVI values at top of the respective NDVI legend.
The final map design should include the following:
Landscape or portrait orientation map sheet (your choice)
Main map frame featuring the difference pixels atop quickmapservices basemap imagery.
An inset map denoting location of the project area.
Titling that explains the location, date range and thematic purpose of the map.
Simple legend for the difference class (-1).
Source the data as follows: Landsat-8 and Landsat-7 analysis imagery courtesy of the U.S. Geological Survey